Determining points of interest using intelligent agents and semantic data

ABSTRACT

A method, a system, and a computer program product are provided for determining points of interest using intelligent agents and semantic data. The method is implemented in a computer infrastructure having computer executable code tangibly embodied on a computer readable storage medium having programming instructions operable for receiving a media data comprising a location data comprising where media was captured. The instructions are also operable for determining at least one point of interest based on the media data, tying the media data to the at least one point of interest, and providing the media data tied to the at least one point of interest to an end user.

TECHNICAL FIELD

The present invention generally relates to a method, a system, and acomputer program product for determining points of interest, and moreparticularly, to a method, a system, and a computer program product fordetermining points of interest using intelligent agents and semanticdata.

BACKGROUND

The availability of Global Position System (GPS) functionality indevices has expanded, along with cellular networks' ability to quicklydetermine the location of these devices. As a result, the latest devicesare embracing the addition of location awareness in their functionality.An example of this advancement is in smartphones that incorporatelocation awareness in many facets of their device functionality.

For example, smartphones may incorporate location awareness in their mapfunctions. Specifically, a smartphone user can view a map based on hisor her determined location, the map including the area that the user iswalking in and/or an indicator (e.g., a pinpoint) on the map where he orshe is located. In another example, smartphones may incorporate locationawareness to recommend restaurants to users. For instance, after a userrequests a smartphone for a restaurant recommendation, the smartphonemay receive from a cellular network its location. The smartphone maythen find and recommend restaurants based on the received locationinstead of simply on food genre, price, and/or reviewer ratings.

Accordingly, incorporating location awareness into device functionalitymay provide various advantages to users. In the previous example of asmartphone recommending restaurants, location awareness may allow usersto conveniently receive information regarding nearby eateries instead ofsolely receiving a list of distant ones. However, while locationawareness has been successfully incorporated into several devicefunctions for user benefit, there is a need for methods and systems toincorporate location awareness into other device functions.

SUMMARY

In a first aspect of the invention, a method is implemented in acomputer infrastructure having computer executable code tangiblyembodied on a computer readable storage medium having programminginstructions operable for receiving a media data including a locationdata including where media was captured. The instructions are alsooperable for determining at least one point of interest based on themedia data, tying the media data to the at least one point of interest,and providing the media data tied to the at least one point of interestto an end user.

In another aspect of the invention, system implemented in hardwareincludes a media agent configured to receive a media data including alocation data of where media was captured. The system also includes anarbiter module configured to receive the media data from the mediaagent, determine at least one point of interest based on the media data,tie the media data to the at least one point of interest, and receivefrom an end user a request for the media related to the at least onepoint of interest. The arbiter module is further configured to providethe user at least one of another media and another media data tied tothe at least one point of interest.

In an additional aspect of the invention, a computer program productincludes a computer usable storage medium having readable program codeembodied in the storage medium. The computer program product alsoincludes at least one component operable to receive a media dataincluding a location data including where media was captured, determineat least one point of interest based on the media data, and tie themedia data to the at least one point of interest. The at least onecomponent is further operable to determine a file based on the mediadata tied to the at least one point of interest, and provide the filefor at least one of a presentation to an end user, tagging the media,and storage.

In yet another aspect of the invention, a method of deploying a systemfor identifying at least one point of interest includes providing acomputer infrastructure, being operable to receive a media dataincluding a location data including where media was captured, anddetermine the at least one point of interest based on the media data.The computer infrastructure is further operable to tie the media data tothe at least one point of interest, and provide the media data tied tothe at least one point of interest to an end user.

In a further aspect of the invention, a computer system for determiningpoints of interest, the system includes a CPU, a computer readablememory and a computer readable storage media. The computer system alsoincludes first program instructions to receive a media data including alocation data of where media was captured and a direction data includinga direction a device was pointing at to capture the media, and secondprogram instructions to determine at least one point of interest basedon the media data. Third program instructions tie the media data to theat least one point of interest, and fourth program instructions providethe media data tied to the at least one point of interest to an enduser. The first, second, and third program instructions are stored onthe computer readable storage media for execution by the CPU via thecomputer readable memory. The second program instructions are furtherconfigured to determine an azimuth-based center of the media based on anintersection of the direction data, determine a geographic center of themedia based on an average midpoint of the location data, and determinesemantic data based on the media data. At least one current point ofinterest is determined based on the media data, and the at least onepoint of interest is determined based on the azimuth-based center, thegeographic center, the semantic data, and the at least one current pointof interest.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The present invention is described in the detailed description whichfollows, in reference to the noted plurality of drawings by way ofnon-limiting examples of exemplary embodiments of the present invention.

FIG. 1 is an illustrative environment for implementing the steps inaccordance with aspects of the invention;

FIG. 2A is an exemplary map used to determine a geographic center of acluster of photographs in accordance with aspects of the invention;

FIG. 2B is the exemplary map of FIG. 2A where an array is shifted oneunit of a grid to the right in accordance with aspects of the invention;

FIG. 2C is the exemplary map of FIG. 2A where the array is shifted toinclude a cluster photographs in accordance with aspects of theinvention;

FIG. 2D is an exemplary map where a smaller grid and array are placed onthe map to find a cluster of photographs in accordance with aspects ofthe invention;

FIG. 2E is an exemplary map including a cluster of the photographs and ageographic center of the cluster in accordance with aspects of theinvention;

FIG. 3 is an exemplary map used to determine an azimuth-based center ofa cluster of photographs in accordance with aspects of the invention;

FIG. 4A shows an exemplary flow in accordance with aspects of theinvention;

FIG. 4B shows an exemplary flow implementing processes for determining ageographic center in accordance with aspects of the invention;

FIG. 5 shows an exemplary flow implementing processes for providing auser requested media and/or a list of requested media; and

FIG. 6 shows another exemplary flow in accordance with aspects of theinvention.

DETAILED DESCRIPTION

The present invention generally relates to a method, a system, and acomputer program product for determining points of interest, and moreparticularly, to a method, a system, and a computer program product fordetermining points of interest using intelligent agents and semanticdata. More specifically, the invention is directed to systematicallyidentifying points of interest using shared media tagged withgeographical identification metadata (i.e., geotagged media), andautomatically tying this media to the identified points of interests. Inimplementation, the invention uses various processes to mathematicallyidentify significant clusters of media. In embodiments, once a clusteris identified, a cluster pattern and/or a direction each device waspointing to capture media may be taken into account in processes used toidentify points of interest routinely captured. In further embodiments,once a point of interest is systematically identified as a specificpinpoint on a map, location information of the pinpoint (e.g., longitudeand latitude) can be correlated with existing points-of-interest maps toretrieve related information such as the name and/or the description(i.e., semantic data) of the identified point of interest.

In embodiments, the present invention can utilize the retrieved semanticdata and/or additional semantic data to make inferences about geotaggedmedia shared during a window of time, one of these inferences beingpoints of interest related to the media. For example, using semanticdata, the system can determine a relevance of pictures around a point ofinterest, and can infer that a picture or group of pictures may berelated to an event, a time, and a point of interest based on thepicture location(s). Although the examples described herein referprimarily to photographs, other types of media or data are contemplatedby the invention, including for example, video, crime data, and eventdata.

By implementing the present invention, media may be systematicallygrouped and/or tagged by identified points of interest and relatedsemantic data, which currently requires significant human interventionto manually tag media. In addition, users can conveniently review andupdate the points of interest and the related semantic data identifiedby the system. For example, in operation, a user can point a device at apoint of interest, capture the point of interest as media, and share themedia by, for instance, uploading the media via a network to the system.The system then instantly matches the shared media to an existingcluster of media and can identify the point of interest related to themedia. Once the point of interest is identified, advantageously, a usercan query the system for media including the point of interest thatalready exist and receive from the system a list of such media. Forinstance, the list of media may include images of the point of interestfrom different vantage points, such as an image of a body of water fromstanding on land and an image of the same body of water from the body ofwater itself. As mentioned above, a user can also receive from thesystem semantic data related to the identified points of interest.Further, law enforcement or emergency services can query the system formedia of a certain occurrence using a specified location within aparticular time window.

System Environment

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

FIG. 1 shows an illustrative environment 10 for managing the processesin accordance with the invention. To this extent, the environment 10includes a server or other computing system 12 that can perform theprocesses described herein. In particular, the server 12 includes acomputing device 14. The computing device 14 can be resident on anetwork infrastructure or computing device of a third party serviceprovider (any of which is generally represented in FIG. 1).

The computing device 14 also includes a processor 20, memory 22A, an I/Ointerface 24, and a bus 26. The memory 22A can include local memoryemployed during actual execution of program code, bulk storage, andcache memories which provide temporary storage of at least some programcode in order to reduce the number of times code must be retrieved frombulk storage during execution. In addition, the computing deviceincludes random access memory (RAM), a read-only memory (ROM), and anoperating system (O/S). The memory (e.g., 22A) may store businessintelligence, data mining, regression analysis and/or modeling andsimulation tools for execution by the processor 20.

The computing device 14 is in communication with the external I/Odevice/resource 28 and the storage system 22B. For example, the I/Odevice 28 can comprise any device that enables an individual to interactwith the computing device 14 (e.g., user interface) or any device thatenables the computing device 14 to communicate with one or more othercomputing devices using any type of communications link. The externalI/O device/resource 28 may be for example, a handheld device, PDA,handset, keyboard etc.

In general, the processor 20 executes computer program code (e.g.,program control 44), which can be stored in the memory 22A and/orstorage system 22B. Moreover, in accordance with aspects of theinvention, the program control 44 controls an arbiter module 105, e.g.,the processes described herein. The arbiter module 105 can beimplemented as one or more program code in the program control 44 storedin memory 22A as separate or combined modules. Additionally, the arbitermodule 105 may be implemented as separate dedicated processors or asingle or several processors to provide the function of these tools.While executing the computer program code, the processor 20 can readand/or write data to/from memory 22A, storage system 22B, and/or I/Ointerface 24. The program code executes the processes of the invention,such as, for example, determining points of interest based onintelligent agents and/or semantic data. The bus 26 provides acommunications link between each of the components in the computingdevice 14.

The computing device 14 can comprise any general purpose computingarticle of manufacture capable of executing computer program codeinstalled thereon (e.g., a personal computer, server, etc.). However, itis understood that the computing device 14 is only representative ofvarious possible equivalent-computing devices that may perform theprocesses described herein. To this extent, in embodiments, thefunctionality provided by the computing device 14 can be implemented bya computing article of manufacture that includes any combination ofgeneral and/or specific purpose hardware and/or computer program code.In each embodiment, the program code and hardware can be created usingstandard programming and engineering techniques, respectively.

Similarly, the computing infrastructure 12 is only illustrative ofvarious types of computer infrastructures for implementing theinvention. For example, in embodiments, the server 12 comprises two ormore computing devices (e.g., a server cluster) that communicate overany type of communications link, such as a network, a shared memory, orthe like, to perform the process described herein. Further, whileperforming the processes described herein, one or more computing deviceson the server 12 can communicate with one or more other computingdevices external to the server 12 using any type of communications link.The communications link can comprise any combination of wired and/orwireless links; any combination of one or more types of networks (e.g.,the Internet, a wide area network, a local area network, a virtualprivate network, etc.); and/or utilize any combination of transmissiontechniques and protocols.

In embodiments of the present invention, the arbiter module 105 may bein communication with a photograph agent 110, a geographic center agent115, an azimuth-based center agent 120, a semantic data agent 125, and acurrent points-of-interest (POI's) agent 130. The photograph agent 110,the geographic center agent 115, the azimuth-based center agent 120, thesemantic data agent 125, and the current POI's agent 130 may each belocated within the server 12 or outside the server 12 in, for example, anetwork 135. In the latter embodiment, the agents 110, 115, 120, 125,and 130 may be implemented in third-party servers and provide theirfunctionalities as third-party services. Alternatively, each of theagents 110, 115, 120, 125, and 130 may be implemented as one or moreprogram code in the program control 44 stored in memory 22A as separateor combined modules. Additionally, the agents 110, 115, 120, 125, and130 may be implemented as separate dedicated processors or a single orseveral processors to provide the function of these agents.

In embodiments, the arbiter module 105 may receive various data from thephotograph agent 110, the geographic center agent 115, the azimuth-basedcenter agent 120, the semantic data agent 125, and the current POI'sagent 130. More specifically, the arbiter module 105 may receivephotograph data from the photograph agent 110, which interfaces with auser via, for instance, the network 135. The process of producingphotograph data begins when a user's device (e.g., a digital camera)captures a photograph of a possible point of interest. In anotherexample, a user's smartphone may include an advanced, integrated digitalcamera, with resolution as high as 8 megapixel, that can capture aphotograph of a possible point of interest. Along with a high-poweredcamera, a smartphone may further include Internet capabilities thatallow a user to quickly and conveniently upload photographs via theInternet to, for instance, the photograph agent 110, and to share themwith his or her social networks, for example.

In addition to capturing and uploading high resolution pictures, a userdevice may tag these pictures with location data (i.e., geotagging)based on where the device is located when the pictures are taken. Insome cases, a user device may also tag each image with direction dataincluding a direction a device was facing when the image was captured.The location data and/or the direction data may be available for viewingalong with the images on many picture sharing websites. The photographmay further be tagged with metadata such as a time and date when thephotograph is taken. All of these tags or data can constitute thephotograph data. The user uploads or sends the photograph data to thephotograph agent 110, which may further process and/or store thephotograph data. The arbiter module 105 may forward the photograph datato the agents 115, 120, 125, and 130 for further processing of thephotograph data, as described herein.

In further embodiments, the arbiter module 105 may receive a geographiccenter of a cluster of photographs from the geographic center agent 115.The geographic center is a location of a center of a cluster ofphotographs, and may include a longitude value and a latitude value. Thegeographic center agent 115 may receive photograph data from the photoagent 110 and/or via the arbiter module 105 and determine the geographiccenter of a cluster of photographs based on the received photograph dataand/or stored photograph data. Further, the geographic center agent 115determines the geographic center using maps including location data ofphotograph data plotted on the maps, as will be discussed more in detailwith respect to FIGS. 2A-2E below. These maps may be uploaded to thegeographic center agent 115 from, for example, the network 135.

In even further embodiments, the arbiter module 105 may receive anazimuth-based center of a cluster of photographs from the azimuth-basedcenter agent 120. The azimuth-based center is a location of a center ofa cluster of photographs and may include a longitude value and alatitude value. Like the geographic center agent 115, the azimuth-basedcenter agent 120 may receive photograph data from the photo agent 110and/or via the arbiter module 105 and determine the azimuth-based centerof a cluster of photographs based on the received photograph data and/orstored photograph data. The azimuth-based center agent 120 may alsodetermine the azimuth-based center using maps including location data ofphotograph data plotted on the maps. In contrast to the geographiccenter agent 115, the azimuth-based center agent 120 additionallyutilizes photograph data including directional data to determine theazimuth-based center, as will be discussed more in detail with respectto FIG. 3 below. The directional data includes a direction that a userdevice is pointed at in order to capture a photograph, and may include,for example, a degree value with respect to the north (N) direction. Thedirectional data may be captured by a user device equipped with amagnetometer, for instance.

In embodiments, the arbiter module 105 may receive semantic data fromthe semantic data agent 125. Semantic data may include, for example, aname and a description of a location and/or a point of interest.Additionally, semantic data may include an event, a time of the event,and/or a date of the event at a location and/or a point of interest. Thesemantic data agent 125 may receive the semantic data from varioussources (e.g., users and services) via the network 135. The semanticdata agent 125 may also receive photograph data from the arbiter module105 and determine which semantic data to return to the arbiter module105 based on the received photograph data. For example, the semanticdata agent 125 may return event descriptions near locations of thegeotagged photograph data.

In further embodiments, the arbiter module 105 may receive current POI'sfrom the current POI's agent 130. The current POI's may include mapsincluding locations, names, and/or descriptions of the current POI's.The current POI's agent 130 may receive the current POI's from varioussources via the network 135. The current POI's agent 130 may furtherreceive photograph data from the arbiter module 105 and determine whichcurrent POI's to return to the arbiter module 105 based on the receivedphotograph data. For instance, the current POI's agent 130 may returncurrent POI's and related information nearby locations of the geotaggedphotograph data.

In embodiments, the arbiter module 105 may use the data received fromthe agents 110, 115, 120, 125, and 130 to identify points of interestrelated to the received photograph data. For example, the arbiter module105 can use the current POI's from the current POI's agent 130 todetermine if any of the location data in the photograph data from thephoto agent 110 are around the locations of the current POI's. This canbe accomplished by using the geographic center or the azimuth-basedcenter of a cluster of photographs from the agents 115 and 120,respectively. The arbiter module 105 can plot a circle, or any otherpredetermined area, around the geographic center or the azimuth-basedcenter to determine if any of the current POI's fall within the circleor the other predetermined area. Alternatively, the arbiter module 105can identify points of interest without utilizing the geographic centeror the azimuth-based center, by determining, for example, whether anycurrent POI's lie within a circle of a specified radius from aphotograph location. The arbiter module 105 can also search locationdata and/or semantic data of the current POI's to determine if any ofthis data matches with the metadata of the photograph data.

In embodiments, after identifying points of interests using theseexemplary processes, the arbiter module 105 may consider these points ofinterests as a set of possible points of interest related to thephotograph data. The arbiter module 105 may then determine a level ofconfidence for each possible point of interest. For instance, thearbiter module 105 may determine a high level of confidence that a pointof interest of the photograph is the Monument A, if the location of thephotograph is the same as the location of the Monument A. If the levelof confidence of a possible point of interest is greater than a or equalto a predetermined threshold, then the arbiter module 105 may designatethe possible point of interest as the actual point of interest relatedto the photograph data, and may tie the photograph data with the actualpoint of interest and any related semantic data.

In further embodiments, the arbiter module 105 may use the semantic datafrom the semantic data agent 125 and the current POI's agent 130 to makeinferences about the location of the photograph data and/or anidentified point of interest. In other words, the arbiter module 105 caninferentially tie the semantic data to the location of the photographdata and/or the identified point of interest. For example, once thearbiter module 105 identifies the Monument A as a point of interest in aphotograph, the arbiter module 105 can infer that semantic dataincluding event information (e.g., the Fourth of July fireworks) is alsorelated to the photograph. The arbiter module 105 may determine a levelof confidence for these inferences based on the relationship between thesemantic data and the photograph data. For instance, the arbiter module105 may determine a high level of confidence in the inference of thephotograph being related to the Fourth of July fireworks if thephotograph data matches the date and the time of the fireworks. If thelevel of confidence of a possible inference is greater than or equal toa predetermined threshold, then the arbiter module 105 may tie thephotograph data with the inference. Inferences can be utilized tostrengthen levels of confidence in other inferences and in possiblepoints of interest.

In embodiments, the arbiter module 105 may place the photograph data,the identified points of interests, its related semantic data, and theinferences in a file 140 written in, for example, a Resource DescriptionFramework (RDF) metadata data model format. The following is anon-limiting, illustrative example of the RDF file 140:

<rdf:RDF>   <geo:Point>     <geo:lat>48.17913602972129</geo:lat>    <geo:long>16.32122039794922</geo:long>   </geo:Point>  <location:Description>     <location:short>Monument A</location:short>    <location:long>   </location:description>   <event:description>    <event:short>Fourth of July</event:short>    <event:long></event:long>       <event:calander>        <event:date>07/04/2009</event:date>        <event:start>20:00</event:start>        <event:end>23:59</event:end>       </event:calander>  <event:description> </rdf:RDF>

The RDF file 140 shown above includes a geographical location of a pointof interest. The RDF file 140 also includes semantic data associatedwith the point of interest including a location description of the pointof interest and a description of an event at the point of interest. Thedescription of the event at the point of interest includes a shortdescription of the event, a date of the event, and a time duration ofthe event.

In further embodiments, the arbiter module 105 may provide the RDF file140 related to photographs to a user for review and/or update via, forexample, the network 135 and an application interface. Also, the arbitermodule 105 may use the RDF file 140 to tag photograph data withadditional metadata include point-of-interest information.Alternatively, the arbiter module 105 may send the RDF file 140 to aprocessing agent within or outside the server 12, where the processingagent tags photographs with the RDF file 140 and/or uses the RDF file140 in further processes. Advantageously, photographs may besystematically grouped and/or tagged by identified points of interestand related semantic data, which typically requires significant humanintervention to manually tag photographs. Furthermore, users with RDFfiles from the system and/or photographs tagged with data from the RDFfiles can conveniently review and update the points of interest ofphotographs and the related semantic data identified by the system.

In another example, a user may request the photograph agent 110 and/orthe arbiter module 105 for photographs of a point of interest on aspecific date (e.g., the Monument A on the Fourth of July). The arbitermodule 105 may search RDF files stored in the system to retrieve a RDFfile including semantic data with the point of interest on the specificdate. The arbiter module 105 may then read a location from the retrievedRDF file and search for photographs within the location stored locallyor externally. Additionally, the arbiter module 105 may send locationdata of the photographs to the azimuth-based center agent 120 toeliminate possible outliers. More particularly, the azimuth-based centeragent 120 may remove locations of the photographs that point in adirection away from the desired point of interest or mark such locationsas low priority in the set of photographs. Advantageously, a user canquery the system for media of the point of interest that already existand receive from the system the requested media and/or a list of suchmedia.

FIG. 2A shows an exemplary map 200 used by the geographic center agent115 to determine a geographic center of a cluster of photographs 205.Before the geographic center agent 115 can determine the geographiccenter, the agent 115 finds a cluster of photographs 205, or multiplelocations of user photographs that congregate to possibly make up apoint of interest. To search for the cluster, the agent 115 placesphotograph data including locations of the photographs 205 on the map200, as datapoints or pinpoints. The agent 115 also places a grid 210 onthe map 200. The agent 115 then places a two-dimensional array 215 ontothe map 200, the array 215 being a predetermined portion of the grid210. In this case, the array 215 is 3 units by 3 units of the grid 210in size, though other sizes are contemplated by the present invention.Next, the agent 115 scans the array 215 to determine if at least apredetermined number of photographs or datapoints were taken within thearray 215 to constitute a cluster of photographs. In this case, thereare no photographs or datapoints taken within the array 215.

FIG. 2B shows the exemplary map 200 where the array 215 is shifted oneunit of the grid 210 to the right. The geographic center agent 115determines again if there are at least a predetermined number ofphotographs taken within the array 215 to constitute a cluster ofphotographs. In this example, there is only one photograph 220 takenwithin the array 215, and that is below the predetermined number ofphotographs required to constitute a cluster of photographs. The agent115 continues to search for a cluster of photographs by shifting thearray 215 to the right until it reaches the rightmost end of the grid210 or a cluster of photographs is found. If the agent 115 reaches therightmost end of the grid 210, the agent 115 shifts the array back tothe leftmost end of the grid 210 but down a unit of the grid 210, andcontinues the search for a cluster of photographs. The array 215 may beshifted in other directions and in units more less than one unit of thegrid 210.

FIG. 2C shows the exemplary map 200 where the array 215 is shifted toinclude the cluster of the photographs 205. That is, the geographiccenter agent 115 determines that there are at least a predeterminednumber of photographs taken within the array 215 to constitute a clusterof photographs. In embodiments, the geographic center agent 115 maydetermine distances between the photographs 205, such as a distance 225,to discern whether any of the photographs is outside the cluster of thephotographs 205. In this example, the agent 115 determines that thephotograph 220 is outside the cluster of the photographs 205 since thedistance 225 is greater than a predetermined distance betweenphotographs for a cluster of photographs.

FIG. 2D shows an exemplary map 230 where a smaller grid 235 is placedwhere the array 215 was last placed once the cluster of the photographs205 is found. To further ensure that the photographs 205, 220 are withina given area, the geographic center agent 115 may perform a moregranulated search of the smaller grid 235 to find a new cluster ofphotographs. The agent 115 also may utilize a smaller array 240 andsearches the area within the array 240 to find the new cluster ofphotographs.

Once the geographic center agent 115 determines that there are at leasta predetermined number of photographs taken within an array toconstitute a cluster of photographs, the agent 115 can use locations ofthe photographs to determine: 1) a non-weighted geographic center and/or2) a weighted geographic center. These geographic centers are centerpoints or locations of a cluster of photographs.

FIG. 2E shows an exemplary map 245 including the cluster of thephotographs 205 and a geographic center 250 of the cluster of thephotographs 205. The geographic center agent 115 determines thegeographic center 250 by placing the locations of the photographs 205into great circle equations including trigonometric functions asdescribed herein. The geographic center 250 may be non-weighted orweighted.

To determine a non-weighted geographic center, or a true center, of acluster of photographs, the geographic center agent 115 uses equationsthat average the x-axis, the y-axis, and the z-axis values (i.e.,Cartesian coordinates) of locations of the photographs (originally inlatitude and longitude) to determine midpoint values for each axis. Theequations for these midpoint values x_(c), y_(c), and z_(c) for thecorresponding x-axis, the y-axis, and z-axis include the following:

$\begin{matrix}{{x_{c} = {\left( {\sum\limits_{x = 1}^{n}{{\cos\left( {lat}_{x} \right)}*{\cos\left( {lon}_{x} \right)}}} \right)/n}},} & (1) \\{{y_{c} = {\left( {\sum\limits_{y = 1}^{n}{{\cos\left( {lat}_{y} \right)}*{\sin\left( {lon}_{y} \right)}}} \right)/n}},{and}} & (2) \\{{z_{c} = {\left( {\sum\limits_{z = 1}^{n}{\sin\left( {lat}_{z} \right)}} \right)/n}},} & (3)\end{matrix}$where n is a number of photographs.

These midpoint values (x_(c), y_(c), z_(c)) constitute a midpoint orcenter location of the cluster of photographs in Cartesian coordinates.To convert this center location of the cluster into longitude long_(c)and latitude lat_(c), the following equations may be used:lon _(c) =a tan 2(y _(c) ,x _(c)), and  (4)lat _(c) =a tan 2(z _(c) ,√{square root over (x_(c) ² +y _(c) ²)}).  (5)where longitude long_(c) and latitude lat_(c) may constitute thegeographic center of the cluster of photographs.

To determine a weighted geographic center of a cluster of photographs,the above equations for a non-weighted geographic center can be modifiedby weighting or deteriorating each point or location of a photographwith weight d, which may be determined as follows:d=365−(days from entered).  (6)

The days from entered equals a number of days that have passed since aphotograph was entered or uploaded into the system. This weightingallows the geographic center agent 115 to give precedence to more recentphotographs stored. Accordingly, weighted equations for midpoint valuesx_(c), y_(c), and z_(c) of the cluster of photographs for thecorresponding x-axis, the y-axis, and z-axis include the following:

$\begin{matrix}{{x_{c} = {\left( {\sum\limits_{x = 1}^{n}{{\cos\left( {{lat}_{x}*d_{x}} \right)}{\cos\left( {{lon}_{x}*d_{x}} \right)}}} \right)/{\sum\limits_{x = 1}^{n}d_{x}}}},} & (7) \\{{y_{c} = {\left( {\sum\limits_{y = 1}^{n}{{\cos\left( {{lat}_{y}*d_{y}} \right)}{\sin\left( {{lon}_{y}*d_{y}} \right)}}} \right)/{\sum\limits_{y = 1}^{n}d_{y}}}},{and}} & (8) \\{{z_{c} = {\left( {\sum\limits_{z = 1}^{n}{\sin\left( {{lat}_{z}*d_{z}} \right)}} \right)/{\sum\limits_{z = 1}^{n}d_{z}}}},} & (9)\end{matrix}$where n is a number of photographs.

Once these weighted midpoint values (x_(c), y_(c), and z_(c)) aredetermined, they may be converted into longitude lon_(c) and latitudelat_(c) using the equations (4) and (5) to obtain the weightedgeographic center of the cluster of photographs.

FIG. 3 shows an exemplary map 300 used by the azimuth-based center agent120 to determine an azimuth-based center of a cluster of photographs305. The azimuth-based center agent 120 uses directional data 310 ofdevices that captured the photographs 305 to determine the azimuth-basedcenter of the cluster of the photographs 305. As discussed above, thedirectional data 310 may be tagged in the photographs 305 as metadataand may be represented as directional vectors on the map 300 as shown.The azimuth-based center agent 120 may determine an intersection of thedirectional data 310 and may then determine that the location of thisintersection is the azimuth-based center of the cluster of thephotographs 305. For example, as shown in the map 300, the intersectionof the directional data 310 may be the location of the “Cascade Patch,”which the azimuth-based center agent 120 may set as the azimuth-basedcenter of the cluster of the photographs 305.

In embodiments, the azimuth-based center agent 120 may use the sameprocess as the geographic center agent 115 to find a cluster ofphotographs on a map before determining an azimuth-based center of thecluster. In addition, the arbiter module 105 may receive the geographiccenter and the azimuth-based center from the respective agents 115 and120 and determine a hybrid center based on the geographic center and theazimuth-based center.

Flow Diagrams

FIGS. 4A-4B and 5-6 show an exemplary flow 400, an exemplary flow 415,an exemplary flow 500, and an exemplary flow 600, respectively, forperforming aspects of the present invention. The steps of FIGS. 4A-4Band 5-6 may be implemented in the environment of FIG. 1, for example.The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

Furthermore, the invention can take the form of a computer programproduct accessible from a computer-usable or computer-readable mediumproviding program code for use by or in connection with a computer orany instruction execution system. The software and/or computer programproduct can be implemented in the environment of FIG. 1. For thepurposes of this description, a computer-usable or computer readablemedium can be any apparatus that can contain, store, communicate,propagate, or transport the program for use by or in connection with theinstruction execution system, apparatus, or device. The medium can be anelectronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system (or apparatus or device) or a propagation medium.Examples of a computer-readable storage medium include a semiconductoror solid state memory, magnetic tape, a removable computer diskette, arandom access memory (RAM), a read-only memory (ROM), a rigid magneticdisk and an optical disk. Current examples of optical disks includecompact disk—read only memory (CD-ROM), compact disc—read/write (CD-R/W)and DVD.

FIG. 4A depicts the exemplary flow 400 for a process in accordance withaspects of the present invention. At step 405, the process of theinvention starts. At step 410, photograph data including metadata suchas location, direction, time, and/or date of at least one photograph isreceived by an arbiter module. The photograph data may be received forat least one user and may be received via a photograph agent.

At step 415, a center of a cluster of photographs may be determinedbased on the received photograph data, by a geographic center agentand/or an azimuth-based center agent. The center may be a geographiccenter and/or an azimuth-based center and may be determined byrespective processing agents. At step 420, data related to thephotograph data may be determined, by a semantic data agent and/or acurrent POI's agent. The related data may include semantic datapertaining to descriptions, events, and other data for locations of thephotograph data. The related data may further include current POI's at,near, and/or within a circle area around a center of locations of thephotograph data. The related data may be received from at least one useror server and may be determined via corresponding processing agents.

At step 425, POI's related to the photograph data are identified basedon the various data received at an arbiter module, such as thephotograph data, the geographic or azimuth-based center, and/or therelated data including the semantic data and/or the current POI's. Atstep 430, a RDF file is determined for the photograph data, by thearbiter module. For example, the RDF file may be determined for eachphotograph taken and may include data that is determined by the arbitermodule to be related to the respective photograph. The RDF file mayinclude location of the photograph, and may include an identified pointof interest at or near the location of the photograph. The RDF file mayalso include a description of the identified point of interestdetermined or inferred to be related to the point of interest. Further,the RDF file may include event information or other informationdetermined or inferred to be related to the point of interest.

At step 435, the RDF file is provided to a user device, anothercomputing device, and/or a server, and may be used to presentinformation related to a photograph to a user via, for example, anapplication interface. Alternatively, the RDF file may be used to tagphotograph data and/or photographs with additional related information.At step 440, the process of the invention ends.

FIG. 4B shows the exemplary flow 415 for a process to determine a centerof a cluster of photographs in accordance with aspects of the presentinvention. This process may be performed by a geographic center agent.At step 445, the process of the invention begins. At step 450,photograph data (specifically datapoints), a grid, and an array areplaced on a map.

At step 455, the array is scanned for the photograph location points. Atstep 460, it is determined whether a number of the photograph datapointsis greater than or equal to NUM, or a predetermined number of photographlocation points that constitute a cluster of photographs. If the numberof the photograph location points is less than NUM, then the processgoes to step 465, where the array is shifted to, for example, one unitof the grid to the right. The process then returns to step 455.

If the number of the photograph location points is greater than or equalto NUM, then the process goes to step 470, where midpoints x_(c), y_(c),and z_(c) are determined for the photograph location points determinedto be a cluster of photographs. At step 475, the midpoints x_(c), y_(c),and z_(c) are converted from Cartesian coordinates to longitude lon_(c)and latitude lat_(c). At step 480, the longitude lon_(c) and latitudelat_(c) are set as a geographic center of the cluster of photographs. Atstep 485, the process of determining the geographic center ends.

FIG. 5 shows the exemplary flow 500 for a process to provide a userrequested media and/or a list of requested media. At step 505, theprocess starts. At step 510, a request for photographs of a point ofinterest on a specific date (e.g., the Monument A on the Fourth of July)is received at a photograph agent and/or an arbiter module. At step 515,the arbiter module searches RDF files stored in the system to retrieve aRDF file including semantic data with the requested point of interest onthe specific date. At step 520, the arbiter module reads a location fromthe retrieved RDF file. At step 525, the arbiter module searches forphotographs and/or photograph data within the read location, thephotographs being stored locally or externally. At step 530, the arbitermodule provides the user any photographs within the read location and/ora list of such photographs via the photograph agent and/or the network.At step 535, the process of providing a user requested media and/or alist of requested media ends.

FIG. 6 shows the exemplary flow 600 for another process in accordancewith aspects of the present invention. At step 605, the process starts.At step 610, photograph data including metadata such as location,direction, time, and/or date of at least one photograph is received bythe system of the present invention. The photograph data may be receivedfor at least one user and may be received via a photograph agent.

At step 615, the arbiter module determines whether there is locationdata in the received photograph data. If there is location data in thephotograph data, then the process continues at step 620. If there is nolocation data in the photograph data, then the process ends at step 655.At step 620, the arbiter module identifies at least one point ofinterest based on the received location data. That is, the arbitermodule determines whether the location data of at least one photographis near current points of interest that may be received from a currentpoints of interest agent.

At step 625, a geographic center agent determines whether there is acluster of photographs in a given area based on the received photographdata and possibly stored photograph data. If there is a cluster ofphotographs, then the process continues at step 630. If there is not acluster of photographs, then the process continues at step 635. At step630, the geographic center agent determines a geographic center and/or aweighted geographic center based on the cluster of the photographs.

At step 635, an azimuth-based center agent determines whether there isdirection data in the received photograph data. If there is directiondata in the photograph data, then the process continues at step 640. Ifthere is no direction data in the photograph data, then the processcontinues at step 645. At step 640, the azimuth-based center agentdetermines an azimuth-based center based on the received direction data.

At step 645, the arbiter module identifies the at least one point ofinterest and inferences (e.g., potential events) based on the receivedlocation data and/or semantic data, which may include the determinedgeographic center, the determined azimuth-based center, the currentpoints of interest, and other semantic data received from variousprocessing agents in the system. Specifically, the arbiter modulecompares the received location data and the semantic data to determinewhether the at least one point of interest and/or the inferences can beinferred. At step 650, the arbiter module determines a RDF file thatincludes the photograph data, the at least one point of interest and theinferences, which may be used to represent or describe a photograph. Atstep 655, the process ends.

In embodiments, a service provider, such as a Solution Integrator, couldoffer to perform the processes described herein. In this case, theservice provider can create, maintain, deploy, support, etc., thecomputer infrastructure that performs the process steps of the inventionfor one or more customers. These customers may be, for example, anybusiness that uses technology. In return, the service provider canreceive payment from the customer(s) under a subscription and/or feeagreement and/or the service provider can receive payment from the saleof advertising content to one or more third parties.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims, if applicable, areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprincipals of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated. Accordingly, while the invention has beendescribed in terms of embodiments, those of skill in the art willrecognize that the invention can be practiced with modifications and inthe spirit and scope of the appended claims.

What is claimed is:
 1. A method implemented in a computer infrastructurehaving computer executable code tangibly embodied on a computer readablestorage medium having programming instructions operable for: receivingmedia data comprising location data comprising where media was captured;determining a center of a cluster of the media, comprising: plotting themedia as data points on a map using the location data; determiningwhether a number of the data points in a defined array on the map isgreater than a predetermined number; and determining a center of thenumber of the data points as the center of the cluster of the media whenthe number is greater than the predetermined number, wherein thedetermining the center of the number of data points comprises weightingor deteriorating each data point of the media with a weight “d” bymultiplying the location data of the media by “d”, and calculating thecenter of the cluster of the media using the weighted location datadetermined for each media; determining at least one point of interestbased on the media data and the center of the cluster of the media;tying the media data to the at least one point of interest; andproviding the media data tied to the at least one point of interest toan end user.
 2. The method of claim 1, wherein the media data furthercomprises a direction data comprising a direction a device was pointingto capture the media.
 3. The method of claim 2, wherein the determiningthe center of the cluster of the media comprises: determining anazimuth-based center of the cluster of the media based on anintersection of the direction data when the number is greater than thepredetermined number; and determining the at least one point of interestbased on the azimuth-based center.
 4. The method of claim 1, wherein thedetermining the center of the cluster of the media comprises:determining a geographic center of the cluster of the media based on anaverage midpoint of the weighted location data when the number isgreater than the predetermined number; and determining the at least onepoint of interest based on the geographic center.
 5. The method of claim4, wherein the determining of the at least one point of interest basedon the geographic center comprises: plotting a predetermined area aroundthe geographic center; and determining whether at least one currentpoint of interest falls within the predetermined area.
 6. The method ofclaim 1, wherein the determining of the at least one point of interestcomprises: determining a semantic data based on the media data; anddetermining the at least one point of interest based on the semanticdata.
 7. The method of claim 1, wherein the determining of the at leastone point of interest comprises: determining at least one current pointof interest based on the media data; and determining the at least onepoint of interest based on the at least one current point of interest.8. The method of claim 1, further comprising tying the media data to asemantic data related to the at least one point of interest, wherein thetying the media data to the semantic data comprises: determining a levelof confidence that the media data is related to the semantic data; andinferring the media data is related to the semantic data if the level ofconfidence is greater than or equal to a predetermined threshold.
 9. Themethod of claim 1, wherein the tying the media data to the at least onepoint of interest comprises tagging the media with the at least onepoint of interest.
 10. The method of claim 1, wherein a service providerat least one of creates, maintains, deploys and supports the computerinfrastructure.
 11. The method of claim 1, wherein steps of claim 1 areprovided by the service provider on a subscription, advertising, and/orfee basis.
 12. A system implemented in hardware, comprising: a mediaagent configured to receive media data comprising location data of wheremedia was captured; a center agent configured to: determine a center ofa cluster of the media, comprising: plotting the media as data points ona map using the location data; determine whether a number of the datapoints in a defined array on the map is greater than a predeterminednumber; and determine a center of the number of the data points as thecenter of the cluster of the media data when the number is greater thanthe predetermined number, wherein the determining the center of thenumber of data points comprises weighting or deteriorating each datapoint of the media with a weight “d” by multiplying the location data ofthe media by “d”, and calculating the center of the cluster of the mediausing the weighted location data determined for each media; and anarbiter module configured to: receive the media data from the mediaagent; receive the center of the cluster of the media from the centeragent; determine at least one point of interest based on the media dataand the center of the cluster of the media; tie the media data to the atleast one point of interest; receive from an end user a request for themedia related to the at least one point of interest; and provide theuser at least one of another media and another media data tied to the atleast one point of interest.
 13. The system of claim 12, wherein: themedia data further comprises a direction data comprising a direction adevice was pointing to capture the media; the center agent is anazimuth-based center agent configured to determine an azimuth -basedcenter of the cluster of the media based on an intersection of thedirection data when the number is greater than the predetermined number;and the arbiter module is further configured to: receive theazimuth-based center from the azimuth-based center agent; and determinethe at least one point of interest based on the azimuth-based center.14. The system of claim 12, wherein the center agent is a geographiccenter agent configured to determine a geographic center of the clusterof the media based on an average midpoint of the weighted location datawhen the number is greater than the predetermined number, wherein thearbiter module is further configured to: receive the geographic centerfrom the geographic center agent; and determine the at least one pointof interest based on the geographic center.
 15. The system of claim 12,further comprising a semantic data agent configured to determine asemantic data based on the media data, wherein the arbiter module isfurther configured to: receive the semantic data from the semantic dataagent; and determine the at least one point of interest based on thesemantic data.
 16. The system of claim 12, further comprising a currentpoints of interest agent configured to determine at least one currentpoint of interest based on the media data, wherein the arbiter module isfurther configured to: receive the at least one current point ofinterest from the current points of interest agent; and determine the atleast one point of interest based on the at least one current point ofinterest.
 17. The system of claim 12, wherein the arbiter module isfurther configured to tie the media data to semantic data related to theat least one point of interest.
 18. The system of claim 12, wherein thearbiter module is further configured to: search for a file comprisingthe at least one point of interest and a location where the anothermedia was captured, from a plurality of files comprising points ofinterests and locations where media related to the points of interestswere captured; read the location from the file; and search for the atleast one of the another media and the another media data based on thelocation.
 19. A computer program product comprising a computer readablestorage medium having readable program code embodied in the storagemedium, the computer program product includes at least one componentoperable to: receive media data comprising location data comprisingwhere media was captured; determine a center of a cluster of the media,comprising: plotting the media as data points on a map using thelocation data; providing a grid and an array on the map, the arrayrepresenting a number of units on the grid; determine whether a numberof the data points in the array on the map is greater than apredetermined number; and determine a center of the number of the datapoints as the center of the cluster of the media data when the number isgreater than the predetermined number, wherein the determining thecenter of the number of data points comprises weighting or deterioratingeach data point of the media with a weight “d” by multiplying thelocation data of the media by “d”, where “d” is a predetermined amountof time minus a number of days that have passed since the media wasentered or uploaded to the system, and calculating the center of thecluster of the media using the weighted location data determined foreach media; determine at least one point of interest based on the mediadata and the center of the cluster of the media; tie the media data tothe at least one point of interest; determine a file based on the mediadata tied to the at least one point of interest; and provide the filefor at least one of a presentation to an end user, tagging the media,and storage.
 20. The computer program product of claim 19, wherein themedia data further comprises a direction data comprising a direction adevice was pointing to capture the media.
 21. A method of deploying asystem for identifying at least one point of interest, comprising:providing a computer infrastructure, being operable to: receive mediadata comprising location data comprising where media was captured;determine a center of a cluster of the media, comprising: plotting themedia as data points on a map using the location data; providing a gridand an array on the map, the array representing a number of units on thegrid; determine whether a number of the data points in the array on themap is greater than a predetermined number; and determine a center ofthe number of the data points as the center of the cluster of the mediadata when the number is greater than the predetermined number, whereinthe determining the center of the number of data points comprisesweighting or deteriorating each data point of the media with a weight“d” by multiplying the location data of the media by “d”, where “d” is apredetermined amount of time minus a number of days that have passedsince the media was entered or uploaded to the system. and calculatingthe center of the cluster of the media using the weighted location datadetermined for each media; determine the at least one point of interestbased on the media data and the center of the cluster of the media; tiethe media data to the at least one point of interest; and provide themedia data tied to the at least one point of interest to an end user.22. The method of claim 21, wherein the media data further comprises adirection data comprising a direction a device was pointing to capturethe media.
 23. A computer system for determining points of interest, thesystem comprising: a CPU, a computer readable memory and a computerreadable storage media; first program instructions to receive media datacomprising location data of where media was captured and a directiondata comprising a direction a device was pointing at to capture themedia; second program instructions to determine a center of a cluster ofthe media, comprising: plotting the media as data points on a map usingthe location data; providing a grid and an array on the map, the arrayrepresenting a number of units on the grid; determine whether a numberof the data points in the array on the map is greater than apredetermined number; and determine a center of the number of the datapoints as the center of the cluster of the media data when the number ofthe data points is greater than the predetermined number, wherein thedetermining the center of the number of data points comprises weightingor deteriorating each data point of the media with a weight “d” bymultiplying the location data of the media by “d”, where “d” is apredetermined amount of time minus a number of clays that have passedsince the media was entered or uploaded to the system, and calculatingthe center of the cluster of the media using the weighted location datadetermined for each media; third program instructions to determine atleast one point of interest based on the media data and the center ofthe cluster of the media data; fourth program instructions to tie themedia data to the at least one point of interest; and fifth programinstructions to provide the media data tied to the at least one point ofinterest to an end user, wherein the first-fifth program instructionsare stored on the computer readable storage media for execution by theCPU via the computer readable memory, wherein second programinstructions are further configured to: determine an azimuth-basedcenter of the cluster of the media based on an intersection of thedirection data when the number of the data points is greater than thepredetermined number; determine a geographic center of the cluster ofthe media based on an average midpoint of the weighted location datawhen the number of the data points is greater than the predeterminednumber; determine semantic data based on the media data; determine atleast one current point of interest based on the media data; anddetermine the at least one point of interest based on the azimuth-basedcenter, the geographic center, the semantic data, and the at least onecurrent point of interest.
 24. The method of claim 1, further comprisingdetermining distances between the media, and determining whether any ofthe media is outside of the cluster of the media based on the distances.25. The method of claim 1, wherein the weighted factor “d” is 365 minusa number of days that have passed since the media was entered oruploaded.